There has been developed for Central America downscaled climate change scenarios using the same methodology, that allows a joint analysis for the whole region. The high number of simulations improves the situation prior to the start of the action, where each country had a limited number of projections that differed in terms of background information, methodology and resolution. A web based viewer allows consultations and download on 37 different climatic variables and derived indices.

CSTools (short for Climate Service Tools) is an R package that contains process-based methods for climate forecast calibration, bias correction, statistical and stochastic downscaling, optimal forecast combination, and multivariate verification, as well as basic and advanced tools to obtain tailored products. In addition to describing the structure and methods in the package, we also present three use cases to illustrate the seasonal climate forecast post-processing for specific purposes.

Climate change projections for precipitation and temperature are a crucial element for stakeholders to make well-informed decisions on adaptation to new climate conditions. In this frame, the Pyrenees constitute a paradigmatic example of mountains undergoing rapid changes in environmental conditions. The impact of the scenarios becomes significant for the second half of the 21st century.

A new regional climate modelling system, HCLIM38, is presented and shown to be applicable in different regions ranging from the tropics to the Arctic. The main focus is on climate simulations at horizontal resolutions between 1 and 4 km, the so-called convection-permitting scales, even though the model can also be used at coarser resolutions. The benefits of simulating climate at convection-permitting scales are shown and are particularly evident for climate extremes.

In the frame of MEDSCOPE project, a seasonal forecast empirical model for the Mediterranean is proposed. It uses a sub regions based set up, with different inputs for every area, from an initial set of global climate indices. However, is configurated to be able to easily incorporate other sources of information. Results show spatially consistent structure, and measurements of its skill shows it performs at the level (and better over some areas) of main dynamical models for seasonal forecasting.